Assessing Drone Return-to-Home Landing Accuracy in a Woodland Landscape

While aerial photography continues to play an integral role in forest management, its data acquisition can now be obtained through an unmanned aerial vehicle (UAV), commonly referred as a drone, instead of conventional manned aircraft. With its feasibility, a drone can be programed to take off, fly over an area following predefined paths and take images, then return to the home spot automatically. When flying over forests, it requires that there is an open space for a vertical takeoff drone to take off vertically and return safely. Hence, the automatic return-to-home feature on the drone is crucial when operating in a woodland landscape. In this project, we assessed the return-to-home landing accuracy based on a permanently marked launch pad nested in a wooded area on the campus of Stephen F. Austin State University in Nacogdoches, Texas. We compared four models of the DJI drone line, with each flown 30 missions over multiple days under different weather conditions. When each drone returned to the home launch spot and landed, the distance and direction from the launch spot to the landing position was measured. Results showed that both the Phantom 4 Advanced and the Spark had superior landing accuracy, whereas the Phantom 3 Advanced was the least accurate trailing behind the Phantom 4 Pro.

Exact and Heuristic Approaches to Surveillance Routing with a Minimum Number of Drones

The rising cost and scarcity of human labor pose challenges in security patrolling tasks, such as facility security. Drones offer a promising solution to replace human patrols. This paper proposes two methods for finding the minimum number of drones required for efficient surveillance routing: an ILP-based method and a greedy method. We evaluate these methods through experiments, comparing the minimum number of required drones and algorithm runtime. The findings indicate that the ILP-based method consistently yields the same or a lower number of drones needed for surveillance compared to the greedy method, with a 73.3% success rate in achieving better results. However, the greedy method consistently finishes within one second, whereas the ILP-based method sometimes significantly increases when dealing with 14 more locations. As a case study, we apply the greedy method to identify the minimum drone surveillance route for the Osaka-Ibaraki Campus of Ritsumeikan University.

Review on Drone-Assisted Air-Quality Monitoring Systems

Drone-aided systems have gained popularity in the last few decades due to their stability in various commercial sectors and military applications. The conventional ambient air quality monitoring stations (AAQMS) are immovable and big. This drawback has been significantly overcome by drone-aided low-cost sensor (LCS) modules. As a result, much research work, media information, and technical notes have been released on drone-aided air quality and ecological monitoring and mapping applications. This work is a sincere effort to provide a comprehensive and structured review of commercial drone applications for air quality and environmental monitoring. The collected scientific and non-scientific information was divided according to the different drone models, sensor types, and payload weights. The payload component is very critical in stablility of the multirotor drones. Most study projects installed inexpensive sensors on drones according to the avilibility of the space on drone frame. After reviewing of multiple environmental applications the common payload range was 0 gm to 4000 gm. The crucial elements are addressed, including their relation to meteorological factors, air isokinetics, propeller-induced downwash, sensor mounting location, ramifications etc. As a result, technical recommendations for AQ monitoring assisted by drones are addressed in the debate part. This work will help researchers and environmentalists choose sensor-specific payloads for drones and mounting locations. Also, it enables advanced methods of monitoring parameters that help policymakers to frame advanced protocols and sensor databases for the environment and ecology.

Drones and Autonomous Vehicles — A New Open-access Journal for Multidisciplinary Research on Autonomous Systems